Revving up Auto Sales with Facebook Topic Data

I recently read an AdExchanger piece that discussed opportunities for the auto-industry to make better use of data in its advertising. The author, Maxwell Knight of Turn, describes the challenges presented by having branding dealt with at a national level and direct-response happening at a regional level. Knight argues that rather than taking the traditional approach of identify individual customers based on searches and clicks, and programmatically serving ads, the automakers would benefit at both the national and the regional level by establishing a “cross feed of anonymized data attached to geography”. Of course, when I hear those words, my ears prick up.

Click and search data are both great sources of insight for the automakers to use in this way, but I think that there is a great deal of potential insight that they are missing out on if they ignore social. In the US, for example, people spend an average of 1.7 hours a day on social media. The biggest social platform is, of course, Facebook which has over 1 billion people active on it every day, posting, commenting, sharing and liking. This represents a huge dataset that the automakers could use to garner insights into their potential customers.

Facebook posts are not for public consumption, but advertisers can get insights from their audiences activity on Facebook in an anonymized and aggregated form using Facebook topic data. Much like the click and search information these insights can be broken down geographically to address the national/regional divide, but they also contain demographic data on age and gender. These factors can be cross referenced with rich data on the content people are engaging with, the links, hashtags and topics they are sharing. The automakers can even analyze insights from the text of post to identify intent to purchase, the features discussed and how they compare to the competition.

For example, an automaker who was advertising in a multilingual area could determine which aspects of cars (style, safety, practicality etc.) people were posting about most in different languages and create ads in those languages that highlighted those aspects of their vehicles. Or a manufacturer could find out which demographic groups were more likely to be engaging with content about their brand and target their advertising to that group. They could also find out which other brands were being considered by that demographic group and tailor their campaigns accordingly, highlighting their strengths against the brands they are genuinely being compared against by their engaged audience.

Combining insights such as these with click and search data, all in an anonymized and aggregated model, gives the automakers the intelligence they need to deliver right-time, right-place ads that actually speak to the audience that are viewing them.